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Conditional generative adversarial network (c-GAN) is one of the best-performing convolutional neural networks (CNN) for the segmentation of lung computed tomography (CT). However, lung segmentation from CT images becomes complicated in the presence of various dense abnormalities. The performance in the presence of dense abnormalities can be improved by tuning the c-GAN architecture and parameters of the network. This study focuses on maximizing lung segmentation performance of a c-GAN segmentation algorithm by configuring and tuning the network using the Taguchi optimization method. We have considered the benchmark interstitial lung disease (ILD) dataset for evaluating the performance of the proposed approach. The comparative performance analysis of the proposed algorithm shows that the proposed algorithms outperform the existing state-of-the-art methods, even in the presence of dense abnormalities in lung CT scans. Furthermore, the proposed approach has been demonstrated for lung segmentation in the presence of large nodules.

Maximization of lung segmentation of generative adversarial network for using taguchi approach